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Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning

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Abstract

Objective

The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.

Methods

ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.

Results

A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.

Conclusion

Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

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Data availability

The data supporting the conclusions are included in the article. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Zipes DP et al (2006) ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death). J Am Coll Cardiol 48(5):e247-346. https://doi.org/10.1016/j.jacc.2006.07.010

    Article  PubMed  Google Scholar 

  2. Kandala J, Oommen C, Kern KB (2017) Sudden cardiac death. Br Med Bull 122(1):5–15. https://doi.org/10.1093/bmb/ldx011

    Article  PubMed  Google Scholar 

  3. Wellens HJ et al (2014) Risk stratification for sudden cardiac death: current status and challenges for the future. Eur Heart J 35(25):1642–1651. https://doi.org/10.1093/eurheartj/ehu176

    Article  PubMed  PubMed Central  Google Scholar 

  4. Monserrat L et al (2003) Non-sustained ventricular tachycardia in hypertrophic cardiomyopathy: an independent marker of sudden death risk in young patients. J Am Coll Cardiol 42(5):873–879. https://doi.org/10.1016/s0735-1097(03)00827-1

    Article  PubMed  Google Scholar 

  5. Corrado D, Link MS, Calkins H (2017) Arrhythmogenic Right Ventricular Cardiomyopathy. N Engl J Med 376(1):61–72. https://doi.org/10.1056/nejmra1509267

    Article  CAS  PubMed  Google Scholar 

  6. Myerburg RJ (2001) Sudden cardiac death: exploring the limits of our knowledge. J Cardiovasc Electrophysiol 12(3):369–381. https://doi.org/10.1046/j.1540-8167.2001.00369.x

    Article  CAS  PubMed  Google Scholar 

  7. Stecker EC et al (2006) Population-based analysis of sudden cardiac death with and without left ventricular systolic dysfunction: two-year findings from the Oregon Sudden Unexpected Death Study. J Am Coll Cardiol 47(6):1161–1166. https://doi.org/10.1016/j.jacc.2005.11.045

    Article  PubMed  Google Scholar 

  8. Khairy P et al (2022) Sudden cardiac death in congenital heart disease. Eur Heart J 43(22):2103–2115. https://doi.org/10.1093/eurheartj/ehac104

    Article  PubMed  Google Scholar 

  9. Basso C et al (2010) Guidelines for autopsy investigation of sudden cardiac death. Pathologica 102(5):391–404

    CAS  PubMed  Google Scholar 

  10. Cao, Z., et al. (2019) Diagnostic Roles of Postmortem cTn I and cTn T in Cardiac Death with Special Regard to Myocardial Infarction: A Systematic Literature Review and Meta-Analysis. Int J Mol Sci 20(13). https://doi.org/10.3390/ijms20133351

  11. Esmaeilzadeh M et al (2022) A Combined Echocardiography Approach for the Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Women With Early-Stage Breast Cancer. JAMA Cardiol 7(3):330–340. https://doi.org/10.1001/jamacardio.2021.5881

    Article  PubMed  PubMed Central  Google Scholar 

  12. Carvajal-Zarrabal O et al (2017) Use of Cardiac Injury Markers in the Postmortem Diagnosis of Sudden Cardiac Death. J Forensic Sci 62(5):1332–1335. https://doi.org/10.1111/1556-4029.13397

    Article  CAS  PubMed  Google Scholar 

  13. Osman J et al (2019) Sudden Cardiac Death (SCD) - risk stratification and prediction with molecular biomarkers. J Biomed Sci 26(1):39. https://doi.org/10.1186/s12929-019-0535-8

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jouven X et al (2001) Circulating nonesterified fatty acid level as a predictive risk factor for sudden death in the population. Circulation 104(7):756–761. https://doi.org/10.1161/hc3201.094151

    Article  CAS  PubMed  Google Scholar 

  15. Tian, M, Cao Z, Pang H (2021) Circular RNAs in Sudden Cardiac Death Related Diseases: Novel Biomarker for Clinical and Forensic Diagnosis. Molecules, 26(4). https://doi.org/10.3390/molecules26041155

  16. Sabatasso S et al (2016) Early markers for myocardial ischemia and sudden cardiac death. Int J Legal Med 130(5):1265–1280. https://doi.org/10.1007/s00414-016-1401-9

    Article  PubMed  Google Scholar 

  17. Polacco M et al (2015) Visualization of myocardial infarction by post-mortem single-organ coronary computed tomography: a feasibility study. Int J Legal Med 129(3):517–524. https://doi.org/10.1007/s00414-014-1085-y

    Article  PubMed  Google Scholar 

  18. Arrive L et al (2016) Postmortem coronary CT angiography. Intensive Care Med 42(8):1293–1294. https://doi.org/10.1007/s00134-016-4376-6

    Article  PubMed  Google Scholar 

  19. de la Grandmaison GL (2006) Is there progress in the autopsy diagnosis of sudden unexpected death in adults? Forensic Sci Int 156(2–3):138–144. https://doi.org/10.1016/j.forsciint.2004.12.024

    Article  PubMed  Google Scholar 

  20. Duckworth E et al (2022) Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer. Anal Chem 94(40):13642–13646. https://doi.org/10.1021/acs.analchem.2c02496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang, R, Wang Y (2021) Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis. Int J Mol Sci, 22(3). https://doi.org/10.3390/ijms22031206

  22. Roy S et al (2017) Simultaneous ATR-FTIR Based Determination of Malaria Parasitemia, Glucose and Urea in Whole Blood Dried onto a Glass Slide. Anal Chem 89(10):5238–5245. https://doi.org/10.1021/acs.analchem.6b04578

    Article  CAS  PubMed  Google Scholar 

  23. Guang P et al (2020) Blood-based FTIR-ATR spectroscopy coupled with extreme gradient boosting for the diagnosis of type 2 diabetes: A STARD compliant diagnosis research. Medicine (Baltimore) 99(15):e19657. https://doi.org/10.1097/md.0000000000019657

    Article  PubMed  Google Scholar 

  24. Mateus PDSN et al (2023) Detection of metabolic syndrome with ATR-FTIR spectroscopy and chemometrics in blood plasma. Spectrochim Acta A Mol Biomol Spectrosc 288:122135. https://doi.org/10.1016/j.saa.2022.122135

    Article  CAS  Google Scholar 

  25. Lin H et al (2018) Identification of Pulmonary Edema in Forensic Autopsy Cases of Sudden Cardiac Death Using Fourier Transform Infrared Microspectroscopy: A Pilot Study. Anal Chem 90(4):2708–2715. https://doi.org/10.1021/acs.analchem.7b04642

    Article  CAS  PubMed  Google Scholar 

  26. Dorling KM, Baker MJ (2013) Highlighting attenuated total reflection Fourier transform infrared spectroscopy for rapid serum analysis. Trends Biotechnol 31(6):327–328. https://doi.org/10.1016/j.tibtech.2013.03.010

    Article  CAS  PubMed  Google Scholar 

  27. Morais C et al (2020) Tutorial: multivariate classification for vibrational spectroscopy in biological samples. Nat Protoc 15(7):2143–2162. https://doi.org/10.1038/s41596-020-0322-8

    Article  CAS  PubMed  Google Scholar 

  28. Villamanca JJ et al (2022) Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples. Appl Spectrosc 76(12):1412–1428. https://doi.org/10.1177/00037028221116083

    Article  CAS  PubMed  Google Scholar 

  29. Ho CS et al (2019) Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat Commun 10(1):4927. https://doi.org/10.1038/s41467-019-12898-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jadhav SA et al (2021) Development of integrated microfluidic platform coupled with Surface-enhanced Raman Spectroscopy for diagnosis of COVID-19. Med Hypotheses 146:110356. https://doi.org/10.1016/j.mehy.2020.110356

    Article  CAS  PubMed  Google Scholar 

  31. Greener JG et al (2022) A guide to machine learning for biologists. Nat Rev Mol Cell Biol 23(1):40–55. https://doi.org/10.1038/s41580-021-00407-0

    Article  CAS  PubMed  Google Scholar 

  32. Ringner M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304. https://doi.org/10.1038/nbt0308-303

    Article  CAS  PubMed  Google Scholar 

  33. Yang Q et al (2017) Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis. Talanta 165:545–552. https://doi.org/10.1016/j.talanta.2017.01.018

    Article  CAS  PubMed  Google Scholar 

  34. Yang X et al (2022) Identification of myocardial fibrosis by ATR-FTIR spectroscopy combined with chemometrics. Spectrochim Acta A Mol Biomol Spectrosc 264:120238. https://doi.org/10.1016/j.saa.2021.120238

    Article  CAS  PubMed  Google Scholar 

  35. Tombolesi N et al (2022) Early cardiac-chamber-specific fingerprints in heart failure with preserved ejection fraction detected by FTIR and Raman spectroscopic techniques. Sci Rep 12(1):3440. https://doi.org/10.1038/s41598-022-07390-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Paraskevaidi M et al (2017) Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood. Proc Natl Acad Sci U S A 114(38):E7929–E7938. https://doi.org/10.1073/pnas.1701517114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Guo, S, et al. (2022) Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers. Biomolecules, 12(12). https://doi.org/10.3390/biom12121815

  38. Davies MJ et al (1989) Factors influencing the presence or absence of acute coronary artery thrombi in sudden ischaemic death. Eur Heart J 10(3):203–208. https://doi.org/10.1093/oxfordjournals.eurheartj.a059467

    Article  CAS  PubMed  Google Scholar 

  39. Holmstrom L et al (2022) Plaque histology and myocardial disease in sudden coronary death: the Fingesture study. Eur Heart J 43(47):4923–4930. https://doi.org/10.1093/eurheartj/ehac533

    Article  PubMed  PubMed Central  Google Scholar 

  40. Holmstrom L et al (2020) Electrocardiographic associations with myocardial fibrosis among sudden cardiac death victims. Heart 106(13):1001–1006. https://doi.org/10.1136/heartjnl-2019-316105

    Article  CAS  PubMed  Google Scholar 

  41. Nedaie A, Najafi AA (2018) Support vector machine with Dirichlet feature map**. Neural Netw 98:87–101. https://doi.org/10.1016/j.neunet.2017.11.006

    Article  PubMed  Google Scholar 

  42. **g-yi TAN CCLA (2023) Case Study of Coronary Heart Disease Classification Prediction Based on SVM Alg. J Med Inf 36(01):37–41

    Google Scholar 

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Acknowledgements

The authors thank the patients and their families for participating in this study.

Funding

This research was supported by grants from the National Natural Science Foundation of China (grant number 82072114) and the Fundamental Research Funds for the Central Universities of Central South University (2023ZZTS0546).

Author information

Authors and Affiliations

  1. Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China

    **angyan Zhang, Jiao **ao, Fengqin Yang, Chengxin Ye, Sile Chen & Yadong Guo

  2. Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, China

    Hongke Qu

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Contributions

The first draft of the manuscript was written by Jiao **ao. **angyan Zhang contributed to conception and design and carried out the analysis and interpretation of data. Fengqin Yang and Hongke Qu revised the draft and approved the revisions. Chengxin Ye, Sile Chen performed acquisition and analysis and interpretation of data. Supervision: Yadong Guo. All authors approved the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Yadong Guo.

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Informed consent

Written informed consent for publishing this scientific report was obtained from the direct relative of the decedent in this case.

Conflict of interest

The authors declare no competing interests.

Ethics approval

The study was approved by the Ethics Committee of Central South University and performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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**angyan Zhang and Jiao **ao are co-first authors and they made equal contributions to this work.

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Zhang, X., **ao, J., Yang, F. et al. Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning. Int J Legal Med 138, 1139–1148 (2024). https://doi.org/10.1007/s00414-023-03118-7

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